Towards a Smarter organization for a Self-servicing Society

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. In this paper, a platform is introduced by combining social-oriented communities and complex-event processing concepts: SELFSERV. Its aim is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track on Emergent Technologies for Ambient Assisted Living (ETAAL

Intelligent event broker: a complex event processing system in big data contexts

In Big Data contexts, many batch and streaming oriented technologies have emerged to deal with the high valuable sources of events, such as Internet of Things (IoT) platforms, the Web, several types of databases, among others. The huge amount of heterogeneous data being constantly generated by a world of interconnected things and the need for (semi)-automated decision-making processes through Complex Event Processing (CEP) and Machine Learning (ML) have raised the need for innovative architectures capable of processing events in a streamlined, scalable, analytical, and integrated way. This paper presents the Intelligent Event Broker, a CEP system built upon flexible and scalable Big Data techniques and technologies, highlighting its system architecture, software packages, and classes. A demonstration case in Bosch’s Industry 4.0 context is presented, detailing how the system can be used to manage and improve the quality of the manufacturing process, showing its usefulness for solving real-world event-oriented problems.This work has been supported by FCT –Fundação para a Ciência e Tecnologiawithin the Project Scope: UID/CEC/00319/2019 and the Doctoral scholarship PD/BDE/135101/2017. This paper uses icons made by Freepik, from www.flaticon.com

Prescriptive Control of Business Processes - New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators

Machine-to-machine emergency system for urban safety

Nowadays most people live in urban areas. As populations grow, demand on the city ecosystem increases, directly affecting the entities responsible for the city control. Challenges like this make leaders adopt ways to engage with the surroundings of their city, making them more prepared and aware. The decisions they make not only directly affect the city in short term, but are also a means to improve the decision making process. This work aimed to develop a system which can act as an emergency and security supervisor in a city, generating alerts to empower entities responsible for disaster management. The system is capable of monitoring data from sensors and provide useful knowledge from it. This work presents an architecture for the collection of data in the Internet of Things (IoT). It delivers the analysis of the used tools and the choices made regarding the implemented system. Also, it provides the necessary inputs for developers to participate in the project, since it describes all the techniques, languages, strategies and programming paradigms used. Finally, it describes the prototype that receives data and processes it to generate alerts with the purpose of warning emergency response teams and the future implementation of a prediction module that can act as a useful tool to better manage the emergency personnel. The completion of the internship allowed the learning of new concepts and techniques, as well as the development of those that were already familiar. With regard to the company, the developed system will integrate the company’s Citibrain platform and will act as a central point, in which, every application (e.g. water management, waste management) can be subscribed to receive alerts

A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

A Big Data perspective on Cyber-Physical Systems for Industry 4.0: modernizing and scaling complex event processing

Doctoral program in Advanced Engineering Systems for IndustryNowadays, the whole industry makes efforts to find the most productive ways of working and it already understood that using the data that is being produced inside and outside the factories is a way to improve the business performance. A set of modern technologies combined with sensor-based communication create the possibility to act according to our needs, precisely at the moment when the data is being produced and processed. Considering the diversity of processes existing in a factory, all of them producing data, Complex Event Processing (CEP) with the capabilities to process that amount of data is needed in the daily work of a factory, to process different types of events and find patterns between them. Although the integration of the Big Data and Complex Event Processing topics is already present in the literature, open challenges in this area were identified, hence the reason for the contribution presented in this thesis. Thereby, this doctoral thesis proposes a system architecture that integrates the CEP concept with a rulebased approach in the Big Data context: the Intelligent Event Broker (IEB). This architecture proposes the use of adequate Big Data technologies in its several components. At the same time, some of the gaps identified in this area were fulfilled, complementing Event Processing with the possibility to use Machine Learning Models that can be integrated in the rules' verification, and also proposing an innovative monitoring system with an immersive visualization component to monitor the IEB and prevent its uncontrolled growth, since there are always several processes inside a factory that can be integrated in the system. The proposed architecture was validated with a demonstration case using, as an example, the Active Lot Release Bosch's system. This demonstration case revealed that it is feasible to implement the proposed architecture and proved the adequate functioning of the IEB system to process Bosch's business processes data and also to monitor its components and the events flowing through those components.Hoje em dia as indústrias esforçam-se para encontrar formas de serem mais produtivas. A utilização dos dados que são produzidos dentro e fora das fábricas já foi identificada como uma forma de melhorar o desempenho do negócio. Um conjunto de tecnologias atuais combinado com a comunicação baseada em sensores cria a possibilidade de se atuar precisamente no momento em que os dados estão a ser produzidos e processados, assegurando resposta às necessidades do negócio. Considerando a diversidade de processos que existem e produzem dados numa fábrica, as capacidades do Processamento de Eventos Complexos (CEP) revelam-se necessárias no quotidiano de uma fábrica, processando diferentes tipos de eventos e encontrando padrões entre os mesmos. Apesar da integração do conceito CEP na era de Big Data ser um tópico já presente na literatura, existem ainda desafios nesta área que foram identificados e que dão origem às contribuições presentes nesta tese. Assim, esta tese de doutoramento propõe uma arquitetura para um sistema que integre o conceito de CEP na era do Big Data, seguindo uma abordagem baseada em regras: o Intelligent Event Broker (IEB). Esta arquitetura propõe a utilização de tecnologias de Big Data que sejam adequadas aos seus diversos componentes. As lacunas identificadas na literatura foram consideradas, complementando o processamento de eventos com a possibilidade de utilizar modelos de Machine Learning com vista a serem integrados na verificação das regras, propondo também um sistema de monitorização inovador composto por um componente de visualização imersiva que permite monitorizar o IEB e prevenir o seu crescimento descontrolado, o que pode acontecer devido à integração do conjunto significativo de processos existentes numa fábrica. A arquitetura proposta foi validada através de um caso de demonstração que usou os dados do Active Lot Release, um sistema da Bosch. Os resultados revelaram a viabilidade da implementação da arquitetura e comprovaram o adequado funcionamento do sistema no que diz respeito ao processamento dos dados dos processos de negócio da Bosch e à monitorização dos componentes do IEB e eventos que fluem através desses.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, the Doctoral scholarship PD/BDE/135101/2017 and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01- 0247-FEDER-039479]

Managing application-level QoS for IoT stream queries in hazardous outdoor environments

While most IoT projects focus on well-controlled environments, this paper focuses on IoT applications in the wild, i.e., rugged outdoor environments. Hazard warnings in outdoor monitoring solutions require reliable pattern detection mechanisms, while data may be streamed from a variety of sensors with intermittent communication. This paper introduces the Morepork system for managing application-level Quality of Service in stream queries for rugged IoT environments. It conceptually treats errors as first class citizens and quantifies the impact on application level. We present a proof of concept implementation, which uses real-world data from New Zealand forestry workers